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<article id="content">
<header>
<h1 class="title">Module <code>silk.models.superpoint_utils</code></h1>
</header>
<section id="section-intro">
<p>Utils functions for the SuperPoint model.</p>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python"># Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.

# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

&#34;&#34;&#34;
Utils functions for the SuperPoint model.
&#34;&#34;&#34;

import cv2
import skimage.io as io
import torch
from silk.cv.homography import HomographicSampler
from silk.models.superpoint import SuperPoint


def warp_points(points: torch.Tensor, homography: torch.Tensor):
    &#34;&#34;&#34;
    Warp the points with the given homography matrix.

    Args:
        points (tensor): the predicted points for an image in the format
            3 x num_pred_points, with a row of x coords, row of y coords, row of probs
        homography (tensor): the 3 x 3 homography matrix connecting two images

    Returns:
        cartesian_points (tensor): the points warped by the homography in the shape
            3 x num_pred_points, with a row of x coords, row of y coords, row of probs
    &#34;&#34;&#34;
    num_points = points.shape[1]

    # convert to 2 x num_pred_points array with x coords row, y coords row
    points1 = points[:2]

    # add row of 1&#39;s for multiplication with the homography
    points1 = torch.vstack((points1, torch.ones(1, num_points, device=points1.device)))

    # calculate homogeneous coordinates by multiplying by the homography
    homogeneous_points = torch.mm(homography, points1)

    # get back to cartesian coordinates by dividing, (optional : KEEPING PROBS AS THIRD ROW)
    cartesian_points = torch.vstack(
        (
            homogeneous_points[0] / homogeneous_points[2],
            homogeneous_points[1] / homogeneous_points[2],
        )
    )
    if points.shape[0] &gt; 2:
        cartesian_points = torch.vstack((cartesian_points, points[2]))

    return cartesian_points


def filter_points(points, img_shape):
    &#34;&#34;&#34;
    Keep only the points whose coordinates are still inside the
    dimensions of img_shape.

    Args:
        points (tensor): the predicted points for an image
        img_shape (tensor): the image size

    Returns:
        points_to_keep (tensor): the points that are still inside
            the boundaries of the img_shape
    &#34;&#34;&#34;
    # we want to get rid of any points that are not in the bounds of the second image
    # the mask will be a tensor of shape [num_points_to_keep]
    mask = (
        # ensure x coordinates are greater than 0 and less than image width
        (points[0] &gt;= 0)
        &amp; (points[0] &lt; img_shape[1])
        # ensure y coordinates are greater than 0 and less than image height
        &amp; (points[1] &gt;= 0)
        &amp; (points[1] &lt; img_shape[0])
    )

    # apply the mask
    points_to_keep = points[:, mask]

    return points_to_keep, mask


def keep_true_points(
    points: torch.Tensor,
    homography: torch.Tensor,
    img_shape: torch.Tensor,
):
    &#34;&#34;&#34;
    Keep only the points whose coordinates when warped by the
    homography are still inside the img_shape dimensions.

    Args:
        points (tensor): the predicted points for an image
        homography (tensor): the 3 x 3 homography matrix connecting
            two images
        img_shape (tensor): the image size (img_height, img_width)

    Returns:
        points_to_keep (tensor): the points that are still inside
            the boundaries of the img_shape after the homography is applied
    &#34;&#34;&#34;

    # first warp the points by the homography
    warped_points = warp_points(points, homography)

    # we want to get rid of any points that are not in the bounds of the second image
    # the mask will be a tensor of shape [num_points_to_keep]
    points_to_keep, mask = filter_points(warped_points, img_shape)

    # need to warp by the inverse homography to get the original coordinates back
    points_to_keep = points[:, mask]

    return points_to_keep, mask


def select_k_best_points(points, k):
    &#34;&#34;&#34;
    Select the k most probable points.

    Args:
        points (tensor): a 3 x num_pred_points tensor where the third row is the
            probabilities for each point
        k (int): the number of points to keep

    Returns:
        points (tensor): a 3 x k tensor with only the k best points selected in
            sorted order of the probabilities
    &#34;&#34;&#34;
    points = points.T

    sorted_indices = torch.argsort(points[:, 2], descending=True)
    sorted_prob = points[sorted_indices]
    start = min(k, points.shape[0])

    sorted_points = sorted_prob[:start]
    sorted_indices = sorted_indices[:start]

    return sorted_points.T, sorted_indices


def max_image_size_downsampled_shape(h, w, max_h, max_w):
    downsampled = False
    if h &gt; max_h or w &gt; max_w:
        hr = max_h / h
        wr = max_w / w

        r = min(hr, wr)

        h = int(h * r)
        w = int(w * r)

        downsampled = True

    return h, w, downsampled


def load_image(file_path, H=None, W=None, max_H=None, max_W=None, as_gray=True):
    &#34;&#34;&#34;
    Helper function to load image from file path and reshape for model input.
    NOTE: Loads the image in grayscale (with 1 input channel).

    Args:
        file_path (str): the image location
        H (int): the reshaped image height
        W (int): the reshaped image width
        max_H (int): maximum height of the loaded image (ignored if H is specified)
        max_W (int): maximum width of the loaded image (ignored if W is specified)

    Returns:
        input_image (tensor): a tensor of shape (1, H, W) for input into the
            SuperPoint model
    &#34;&#34;&#34;
    input_image = io.imread(file_path, as_gray=as_gray)

    if (W is not None) and (H is not None):
        input_image = cv2.resize(input_image, (W, H), interpolation=cv2.INTER_AREA)
    elif (max_W is not None) or (max_H is not None):
        max_H = input_image.shape[0] if max_H is None else max_H
        max_W = input_image.shape[1] if max_W is None else max_W

        nh, nw, downsampled = max_image_size_downsampled_shape(
            input_image.shape[0],
            input_image.shape[1],
            max_H,
            max_W,
        )

        if downsampled:
            input_image = cv2.resize(
                input_image,
                (nw, nh),
                interpolation=cv2.INTER_AREA,
            )

    if not as_gray:
        return input_image.transpose((2, 0, 1))

    input_image = input_image.astype(&#34;float32&#34;)
    input_image = torch.from_numpy(input_image)
    if as_gray:
        input_image = input_image.view(1, input_image.shape[-2], input_image.shape[-1])

    return input_image


def _process_output_new(model, images, sparse=True):
    if sparse:
        if isinstance(model, SuperPoint):
            outputs = (&#34;positions&#34;, &#34;sparse_descriptors&#34;)
        else:
            outputs = (&#34;sparse_positions&#34;, &#34;sparse_descriptors&#34;)
    else:
        outputs = (&#34;dense_positions&#34;, &#34;dense_descriptors&#34;)

    positions, descriptors = model.forward_flow(
        images,
        outputs=outputs,
    )

    assert len(positions) == 1
    assert len(descriptors) == 1

    positions = positions[0]
    descriptors = descriptors[0]

    return positions, descriptors


def get_dense_positions(h, w, device, batch_size=None):
    dense_positions = HomographicSampler._create_meshgrid(
        w,
        h,
        device=device,
        normalized=False,
    )
    dense_positions = dense_positions.permute(0, 2, 1, 3)
    dense_positions = dense_positions.reshape(-1, 2)
    dense_positions = dense_positions.unsqueeze(0)

    if batch_size is not None:
        dense_positions = dense_positions.expand(batch_size, -1, -1)

    return dense_positions</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="silk.models.superpoint_utils.filter_points"><code class="name flex">
<span>def <span class="ident">filter_points</span></span>(<span>points, img_shape)</span>
</code></dt>
<dd>
<div class="desc"><p>Keep only the points whose coordinates are still inside the
dimensions of img_shape.</p>
<h2 id="args">Args</h2>
<dl>
<dt><strong><code>points</code></strong> :&ensp;<code>tensor</code></dt>
<dd>the predicted points for an image</dd>
<dt><strong><code>img_shape</code></strong> :&ensp;<code>tensor</code></dt>
<dd>the image size</dd>
</dl>
<h2 id="returns">Returns</h2>
<p>points_to_keep (tensor): the points that are still inside
the boundaries of the img_shape</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def filter_points(points, img_shape):
    &#34;&#34;&#34;
    Keep only the points whose coordinates are still inside the
    dimensions of img_shape.

    Args:
        points (tensor): the predicted points for an image
        img_shape (tensor): the image size

    Returns:
        points_to_keep (tensor): the points that are still inside
            the boundaries of the img_shape
    &#34;&#34;&#34;
    # we want to get rid of any points that are not in the bounds of the second image
    # the mask will be a tensor of shape [num_points_to_keep]
    mask = (
        # ensure x coordinates are greater than 0 and less than image width
        (points[0] &gt;= 0)
        &amp; (points[0] &lt; img_shape[1])
        # ensure y coordinates are greater than 0 and less than image height
        &amp; (points[1] &gt;= 0)
        &amp; (points[1] &lt; img_shape[0])
    )

    # apply the mask
    points_to_keep = points[:, mask]

    return points_to_keep, mask</code></pre>
</details>
</dd>
<dt id="silk.models.superpoint_utils.get_dense_positions"><code class="name flex">
<span>def <span class="ident">get_dense_positions</span></span>(<span>h, w, device, batch_size=None)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def get_dense_positions(h, w, device, batch_size=None):
    dense_positions = HomographicSampler._create_meshgrid(
        w,
        h,
        device=device,
        normalized=False,
    )
    dense_positions = dense_positions.permute(0, 2, 1, 3)
    dense_positions = dense_positions.reshape(-1, 2)
    dense_positions = dense_positions.unsqueeze(0)

    if batch_size is not None:
        dense_positions = dense_positions.expand(batch_size, -1, -1)

    return dense_positions</code></pre>
</details>
</dd>
<dt id="silk.models.superpoint_utils.keep_true_points"><code class="name flex">
<span>def <span class="ident">keep_true_points</span></span>(<span>points: torch.Tensor, homography: torch.Tensor, img_shape: torch.Tensor)</span>
</code></dt>
<dd>
<div class="desc"><p>Keep only the points whose coordinates when warped by the
homography are still inside the img_shape dimensions.</p>
<h2 id="args">Args</h2>
<dl>
<dt><strong><code>points</code></strong> :&ensp;<code>tensor</code></dt>
<dd>the predicted points for an image</dd>
<dt><strong><code>homography</code></strong> :&ensp;<code>tensor</code></dt>
<dd>the 3 x 3 homography matrix connecting
two images</dd>
<dt><strong><code>img_shape</code></strong> :&ensp;<code>tensor</code></dt>
<dd>the image size (img_height, img_width)</dd>
</dl>
<h2 id="returns">Returns</h2>
<p>points_to_keep (tensor): the points that are still inside
the boundaries of the img_shape after the homography is applied</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def keep_true_points(
    points: torch.Tensor,
    homography: torch.Tensor,
    img_shape: torch.Tensor,
):
    &#34;&#34;&#34;
    Keep only the points whose coordinates when warped by the
    homography are still inside the img_shape dimensions.

    Args:
        points (tensor): the predicted points for an image
        homography (tensor): the 3 x 3 homography matrix connecting
            two images
        img_shape (tensor): the image size (img_height, img_width)

    Returns:
        points_to_keep (tensor): the points that are still inside
            the boundaries of the img_shape after the homography is applied
    &#34;&#34;&#34;

    # first warp the points by the homography
    warped_points = warp_points(points, homography)

    # we want to get rid of any points that are not in the bounds of the second image
    # the mask will be a tensor of shape [num_points_to_keep]
    points_to_keep, mask = filter_points(warped_points, img_shape)

    # need to warp by the inverse homography to get the original coordinates back
    points_to_keep = points[:, mask]

    return points_to_keep, mask</code></pre>
</details>
</dd>
<dt id="silk.models.superpoint_utils.load_image"><code class="name flex">
<span>def <span class="ident">load_image</span></span>(<span>file_path, H=None, W=None, max_H=None, max_W=None, as_gray=True)</span>
</code></dt>
<dd>
<div class="desc"><p>Helper function to load image from file path and reshape for model input.
NOTE: Loads the image in grayscale (with 1 input channel).</p>
<h2 id="args">Args</h2>
<dl>
<dt><strong><code>file_path</code></strong> :&ensp;<code>str</code></dt>
<dd>the image location</dd>
<dt><strong><code>H</code></strong> :&ensp;<code>int</code></dt>
<dd>the reshaped image height</dd>
<dt><strong><code>W</code></strong> :&ensp;<code>int</code></dt>
<dd>the reshaped image width</dd>
<dt><strong><code>max_H</code></strong> :&ensp;<code>int</code></dt>
<dd>maximum height of the loaded image (ignored if H is specified)</dd>
<dt><strong><code>max_W</code></strong> :&ensp;<code>int</code></dt>
<dd>maximum width of the loaded image (ignored if W is specified)</dd>
</dl>
<h2 id="returns">Returns</h2>
<p>input_image (tensor): a tensor of shape (1, H, W) for input into the
SuperPoint model</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def load_image(file_path, H=None, W=None, max_H=None, max_W=None, as_gray=True):
    &#34;&#34;&#34;
    Helper function to load image from file path and reshape for model input.
    NOTE: Loads the image in grayscale (with 1 input channel).

    Args:
        file_path (str): the image location
        H (int): the reshaped image height
        W (int): the reshaped image width
        max_H (int): maximum height of the loaded image (ignored if H is specified)
        max_W (int): maximum width of the loaded image (ignored if W is specified)

    Returns:
        input_image (tensor): a tensor of shape (1, H, W) for input into the
            SuperPoint model
    &#34;&#34;&#34;
    input_image = io.imread(file_path, as_gray=as_gray)

    if (W is not None) and (H is not None):
        input_image = cv2.resize(input_image, (W, H), interpolation=cv2.INTER_AREA)
    elif (max_W is not None) or (max_H is not None):
        max_H = input_image.shape[0] if max_H is None else max_H
        max_W = input_image.shape[1] if max_W is None else max_W

        nh, nw, downsampled = max_image_size_downsampled_shape(
            input_image.shape[0],
            input_image.shape[1],
            max_H,
            max_W,
        )

        if downsampled:
            input_image = cv2.resize(
                input_image,
                (nw, nh),
                interpolation=cv2.INTER_AREA,
            )

    if not as_gray:
        return input_image.transpose((2, 0, 1))

    input_image = input_image.astype(&#34;float32&#34;)
    input_image = torch.from_numpy(input_image)
    if as_gray:
        input_image = input_image.view(1, input_image.shape[-2], input_image.shape[-1])

    return input_image</code></pre>
</details>
</dd>
<dt id="silk.models.superpoint_utils.max_image_size_downsampled_shape"><code class="name flex">
<span>def <span class="ident">max_image_size_downsampled_shape</span></span>(<span>h, w, max_h, max_w)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def max_image_size_downsampled_shape(h, w, max_h, max_w):
    downsampled = False
    if h &gt; max_h or w &gt; max_w:
        hr = max_h / h
        wr = max_w / w

        r = min(hr, wr)

        h = int(h * r)
        w = int(w * r)

        downsampled = True

    return h, w, downsampled</code></pre>
</details>
</dd>
<dt id="silk.models.superpoint_utils.select_k_best_points"><code class="name flex">
<span>def <span class="ident">select_k_best_points</span></span>(<span>points, k)</span>
</code></dt>
<dd>
<div class="desc"><p>Select the k most probable points.</p>
<h2 id="args">Args</h2>
<dl>
<dt><strong><code>points</code></strong> :&ensp;<code>tensor</code></dt>
<dd>a 3 x num_pred_points tensor where the third row is the
probabilities for each point</dd>
<dt><strong><code>k</code></strong> :&ensp;<code>int</code></dt>
<dd>the number of points to keep</dd>
</dl>
<h2 id="returns">Returns</h2>
<p>points (tensor): a 3 x k tensor with only the k best points selected in
sorted order of the probabilities</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def select_k_best_points(points, k):
    &#34;&#34;&#34;
    Select the k most probable points.

    Args:
        points (tensor): a 3 x num_pred_points tensor where the third row is the
            probabilities for each point
        k (int): the number of points to keep

    Returns:
        points (tensor): a 3 x k tensor with only the k best points selected in
            sorted order of the probabilities
    &#34;&#34;&#34;
    points = points.T

    sorted_indices = torch.argsort(points[:, 2], descending=True)
    sorted_prob = points[sorted_indices]
    start = min(k, points.shape[0])

    sorted_points = sorted_prob[:start]
    sorted_indices = sorted_indices[:start]

    return sorted_points.T, sorted_indices</code></pre>
</details>
</dd>
<dt id="silk.models.superpoint_utils.warp_points"><code class="name flex">
<span>def <span class="ident">warp_points</span></span>(<span>points: torch.Tensor, homography: torch.Tensor)</span>
</code></dt>
<dd>
<div class="desc"><p>Warp the points with the given homography matrix.</p>
<h2 id="args">Args</h2>
<dl>
<dt><strong><code>points</code></strong> :&ensp;<code>tensor</code></dt>
<dd>the predicted points for an image in the format
3 x num_pred_points, with a row of x coords, row of y coords, row of probs</dd>
<dt><strong><code>homography</code></strong> :&ensp;<code>tensor</code></dt>
<dd>the 3 x 3 homography matrix connecting two images</dd>
</dl>
<h2 id="returns">Returns</h2>
<p>cartesian_points (tensor): the points warped by the homography in the shape
3 x num_pred_points, with a row of x coords, row of y coords, row of probs</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def warp_points(points: torch.Tensor, homography: torch.Tensor):
    &#34;&#34;&#34;
    Warp the points with the given homography matrix.

    Args:
        points (tensor): the predicted points for an image in the format
            3 x num_pred_points, with a row of x coords, row of y coords, row of probs
        homography (tensor): the 3 x 3 homography matrix connecting two images

    Returns:
        cartesian_points (tensor): the points warped by the homography in the shape
            3 x num_pred_points, with a row of x coords, row of y coords, row of probs
    &#34;&#34;&#34;
    num_points = points.shape[1]

    # convert to 2 x num_pred_points array with x coords row, y coords row
    points1 = points[:2]

    # add row of 1&#39;s for multiplication with the homography
    points1 = torch.vstack((points1, torch.ones(1, num_points, device=points1.device)))

    # calculate homogeneous coordinates by multiplying by the homography
    homogeneous_points = torch.mm(homography, points1)

    # get back to cartesian coordinates by dividing, (optional : KEEPING PROBS AS THIRD ROW)
    cartesian_points = torch.vstack(
        (
            homogeneous_points[0] / homogeneous_points[2],
            homogeneous_points[1] / homogeneous_points[2],
        )
    )
    if points.shape[0] &gt; 2:
        cartesian_points = torch.vstack((cartesian_points, points[2]))

    return cartesian_points</code></pre>
</details>
</dd>
</dl>
</section>
<section>
</section>
</article>
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<h1>Index</h1>
<div class="toc">
<ul></ul>
</div>
<ul id="index">
<li><h3>Super-module</h3>
<ul>
<li><code><a title="silk.models" href="index.html">silk.models</a></code></li>
</ul>
</li>
<li><h3><a href="#header-functions">Functions</a></h3>
<ul class="">
<li><code><a title="silk.models.superpoint_utils.filter_points" href="#silk.models.superpoint_utils.filter_points">filter_points</a></code></li>
<li><code><a title="silk.models.superpoint_utils.get_dense_positions" href="#silk.models.superpoint_utils.get_dense_positions">get_dense_positions</a></code></li>
<li><code><a title="silk.models.superpoint_utils.keep_true_points" href="#silk.models.superpoint_utils.keep_true_points">keep_true_points</a></code></li>
<li><code><a title="silk.models.superpoint_utils.load_image" href="#silk.models.superpoint_utils.load_image">load_image</a></code></li>
<li><code><a title="silk.models.superpoint_utils.max_image_size_downsampled_shape" href="#silk.models.superpoint_utils.max_image_size_downsampled_shape">max_image_size_downsampled_shape</a></code></li>
<li><code><a title="silk.models.superpoint_utils.select_k_best_points" href="#silk.models.superpoint_utils.select_k_best_points">select_k_best_points</a></code></li>
<li><code><a title="silk.models.superpoint_utils.warp_points" href="#silk.models.superpoint_utils.warp_points">warp_points</a></code></li>
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